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Dynamic Commodity Timing Strategies

Recent research documents that commodities are good diversifiers in traditional investment portfolios: overall portfolio risk is reduced while less than proportional return is sacrificed. These studies generally find a relatively high volatility in commodity returns, which implies a huge potential for tactical strategies. In this paper we investigate timing strategies with commodity futures using factors directly related to the stance of the business cycle, the monetary environment and the sentiment of the market. We use a dynamic model selection procedure in the spirit of the recursive modeling approach of Pesaran and Timmermann [1995]. However, instead of using in-sample model selection criteria, we build on the extensions of Bauer, Derwall and Molenaar [2004] by introducing an out-of-sample model training period to select optimal models. The best models from this training period are used to generate forecasts in a subsequent trading period. Our results show that the variation in commodity future returns is sufficiently predictable to be exploited by a realistic timing strategy.